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» Fitness Sharing in Genetic Programming
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FLAIRS
2006
13 years 9 months ago
Improving Modularity in Genetic Programming Using Graph-Based Data Mining
We propose to improve the efficiency of genetic programming, a method to automatically evolve computer programs. We use graph-based data mining to identify common aspects of highl...
Istvan Jonyer, Akiko Himes
GECCO
2006
Springer
156views Optimization» more  GECCO 2006»
13 years 11 months ago
Improving GP classifier generalization using a cluster separation metric
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
Ashley George, Malcolm I. Heywood
GECCO
2010
Springer
172views Optimization» more  GECCO 2010»
14 years 14 days ago
Designing better fitness functions for automated program repair
Evolutionary methods have been used to repair programs automatically, with promising results. However, the fitness function used to achieve these results was based on a few simpl...
Ethan Fast, Claire Le Goues, Stephanie Forrest, We...
GECCO
2010
Springer
169views Optimization» more  GECCO 2010»
13 years 11 months ago
Robust symbolic regression with affine arithmetic
We use affine arithmetic to improve both the performance and the robustness of genetic programming for symbolic regression. During evolution, we use affine arithmetic to analyze e...
Cassio Pennachin, Moshe Looks, João A. de V...
GECCO
2008
Springer
133views Optimization» more  GECCO 2008»
13 years 8 months ago
Using feature-based fitness evaluation in symbolic regression with added noise
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the depe...
Janine H. Imada, Brian J. Ross